Predicting more than one token at a time is an interesting paradigm of active research. If successful, it would dramatically improve generation time for many large language models. The approach in this post, which mirrors consistency models from image synthetics, attempts to use a parallel decoding strategy on fine-tuned LLMs to speed up generation. Early results match speculative decoding performance of 3x.